As the role of ontology in a multilingual setting
becomes important to Semantic Web development, it becomes
necessary to understand and model how an original conceptual
meaning of a Source Language word is conveyed into a Target
Language translation. Terminological ontology [1] is a tool
used for knowledge sharing and domain-specific translation,
and could potentially be suitable for simulating the cognitive
models explaining real-world inter-cultural communication
scenarios. In this paper, a framework referred to as the
Relevance Theory of Communication [2] is contrasted to an
empirical study applying Tversky´s contrast model [3] to datasets
obtained from the terminological ontology. The results
indicate that the alignment of two language-dependent
terminological ontologies is a potential method for optimizing
the relevance required in inter-cultural communication, in
other words, for identifying corresponding concepts existing in
two remote cultures.

This paper analyzes patterns of conceptualizations
possessed by different groups of subjects. The eventual goal of
this work is to dynamically learn and structure semantic representations
for groups of people sharing domain knowledge. In
this paper, we conduct a survey for collecting data representing
semantic representations of 34 subjects with different profiles
in gender and educational background. The collected data is
analyzed by an approach combining two extended versions of
the Infinite Relational Model (Kemp et al. 2006) [1]: multiarray
Infinite Relational Model (Mørup et al. 2010) [2] and
normal Infinite Relational Model (Herlau et al. 2012) [3].
Results indicate that the employed approach not only localizes
similar patterns of conceptualization within a group of subjects
having a common profile, but also identifies differences in
conceptualization across different subject groups.

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Cross-lingual concept mapping based on the information receiver’s prior-knowledge

Glückstad, Fumiko Kano(Frederiksberg, 2012)

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Resume:

A Japanese acquaintance who has been living in Denmark for more than
40 years formulated his difficult mission of undertaking translation tasks
in the following way: “Once I deeply understood the two cultures [Denmark
and Japan] and the cultural differences/nuances of conceptual
meanings existing in the two countries, it became impossible for me to
translate culturally-specific terms into the other language. Existing language
resources [dictionaries etc.] are in this context useless”. What he
was frustratingly expressing is that it becomes virtually an impossible
task to precisely translate or convey the meaning of a Culturally-Specific
Concept (CSC) if no exact equivalent concept exists in the Target Language
(TL) culture. Despite this inherent frustration, communicators or
translators are still required to convey such CSCs into a TL in an optimal
manner such that a TL reader can instantly infer the original meaning of a
given Source Language (SL) concept. In short, the key issue is whether
there can be found a way to solve this inherently frustrating situation
which even skilled human translators cannot easily cope with ?
The challenge of translating CSCs from an SL is not only caused by the
absence of equivalent concepts in a TL culture, but also due to differences
of the background knowledge possessed by the two parties involved in a
cross-cultural communication scenario. Sperber & Wilson (1986) emphasize
that, although all humans live in the physical world, mental representations
are constructed differently due to differences in our close environment
and our different cognitive abilities. Because people use different
languages and have mastered different concepts, the way they construct
representations and make inference is also dissimilar. Since an individual
possesses a total cognitive environment that is the set of facts
based on his/her perceptual ability, inferential ability, actual awareness of
facts, knowledge he/she has acquired and so on, it is much easier to
achieve a so-called “asymmetric” coordination between communicator
and audience (Sperber & Wilson, 1986)....

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Values are crucial for explaining the motivational basis of human attitudes and behavior, as well as social and personal
organization. This project investigates methods to analyze values possessed by diverse individuals residing in several
societies based in Japan and other foreign countries. The aim is to identify useful intercultural data analysis methods to
examine the heterogeneity of societies within and across countries based on advanced AI technologies such as machine
learning and ontology technologies. Our intercultural data analysis project is based on the publicly available data such as
World Value Survey and European Social Survey. The project eventually aims at developing an intercultural data analysis
tool for public and private service providers to identify potential target consumer segments of services/products and to
indicate preferences of the potential customers in a foreign market.

This work presents a conceptual framework for
learning an ontological structure of domain knowledge, which
combines Jaccard similarity coefficient with the Infinite Relational
Model (IRM) by (Kemp et al. 2006) and its extended
model, i.e. the normal-Infinite Relational Model (n-
IRM) by (Herlau et al. 2012). The proposed approach is applied
to a dataset where legal concepts related to the Japanese
educational system are defined by the Japanese authorities
according to the International Standard Classification of Education
(ISCED). Results indicate that the proposed approach
effectively structures features for defining groups of concepts in
several levels (i.e., concept, category, abstract category levels)
from which an ontological structure is systematically visualized
as a lattice graph based on the Formal Concept Analysis (FCA)
by (Ganter and Wille 1997).